OpenRaiser/Intern-Atlas huggingface dataset:
Social Engineering Attacks: A Systemisation of Knowledge on People Against Humans
Venue: arXiv cs.CR 2025
Year: 2025

Our systematisation of knowledge on Social Engineering Attacks (SEAs), identifies the human, organisational, and adversarial dimensions of cyber threats. It addresses the growing risks posed by SEAs, highly relevant in the context physical cyber places, such as travellers at airports and residents in smart cities, and synthesizes findings from peer reviewed studies, industry and government reports to inform effective countermeasures that can be embedded into future smart city strategies. SEAs increasingly sidestep technical controls by weaponising leaked personal data and behavioural cues, an urgency underscored by the Optus, Medibank and now Qantas (2025) mega breaches that placed millions of personal records in criminals' hands. Our review surfaces three critical dimensions: (i) human factors of knowledge, abilities and behaviours (KAB) (ii) organisational culture and informal norms that shape those behaviours and (iii) attacker motivations, techniques and return on investment calculations. Our contributions are threefold: (1) TriLayer Systematisation: to the best of our knowledge, we are the first to unify KAB metrics, cultural drivers and attacker economics into a single analytical lens, enabling practitioners to see how vulnerabilities, norms and threat incentives coevolve. (2) Risk Weighted HAISQ Meta analysis: By normalising and ranking HAISQ scores across recent field studies, we reveal persistent high risk clusters (Internet and Social Media use) and propose impact weightings that make the instrument predictive rather than descriptive. (3) Adaptive 'Segment and Simulate' Training Blueprint: Building on clustering evidence, we outline a differentiated programme that matches low, medium, high risk user cohorts to experiential learning packages including phishing simulations, gamified challenges and realtime feedback thereby aligning effort with measured exposure.
From Imitation to Innovation: The Divergent Paths of Techno in Germany and the USA
Venue: arXiv cs.SD 2025
Year: 2025

Many documentaries on early house and techno music exist. Here, protagonists from the scenes describe key elements and events that affected the evolution of the music. In the research community, there is consensus that such descriptions have to be examined critically. Yet, there have not been attempts to validate such statements on the basis of audio analyses. In this study, over 9,000 early house and techno tracks from Germany and the United States of America are analyzed using recording studio features, machine learning and inferential statistics. Three observations can be made: 1.) German and US house/techno music are distinct, 2.) US styles are much more alike, and 3.) scarcely evolved over time compared to German house/techno regarding the recording studio features. These findings are in agreement with documented statements and thus provide an audio-based perspective on why techno became a mass phenomenon in Germany but remained a fringe phenomenon in the USA. Observations like these can help the music industry estimate whether new trends will experience a breakthrough or disappear.
A General Metric-Space Formulation of the Time Warp Edit Distance (TWED)
Venue: arXiv cs.IR 2025
Year: 2025

This short technical note presents a formal generalization of the Time Warp Edit Distance (TWED) proposed by Marteau (2009) to arbitrary metric spaces. By viewing both the observation and temporal domains as metric spaces $(X, d)$ and $(T, Δ)$, we define a Generalized TWED (GTWED) that remains a true metric under mild assumptions. We provide self-contained proofs of its metric properties and show that the classical TWED is recovered as a special case when $X = \mathbb{R}^d$, $T \subset \mathbb{R}$, and $g(x) = x$. This note focuses on the theoretical structure of GTWED and its implications for extending elastic distances beyond time series, which enables the use of TWED-like metrics on sequences over arbitrary domains such as symbolic data, manifolds, or embeddings.
Cross-Document Topic-Aligned Chunking for Retrieval-Augmented Generation
Venue: arXiv cs.IR 2025
Year: 2025

Chunking quality determines RAG system performance. Current methods partition documents individually, but complex queries need information scattered across multiple sources: the knowledge fragmentation problem. We introduce Cross-Document Topic-Aligned (CDTA) chunking, which reconstructs knowledge at the corpus level. It first identifies topics across documents, maps segments to each topic, and synthesizes them into unified chunks. On HotpotQA multi-hop reasoning, our method reached 0.93 faithfulness versus 0.83 for contextual retrieval and 0.78 for semantic chunking, a 12% improvement over current industry best practice (p < 0.05). On UAE Legal texts, it reached 0.94 faithfulness with 0.93 citation accuracy. At k = 3, it maintains 0.91 faithfulness while semantic methods drop to 0.68, with a single CDTA chunk containing information requiring multiple traditional fragments. Indexing costs are higher, but synthesis produces information-dense chunks that reduce query-time retrieval needs. For high-query-volume applications with distributed knowledge, cross-document synthesis improves measurably over within-document optimization.
Transforming User Defined Criteria into Explainable Indicators with an Integrated LLM AHP System
Venue: arXiv cs.IR 2025
Year: 2025

Evaluating complex texts across domains requires converting user defined criteria into quantitative, explainable indicators, which is a persistent challenge in search and recommendation systems. Single prompt LLM evaluations suffer from complexity and latency issues, while criterion specific decomposition approaches rely on naive averaging or opaque black-box aggregation methods. We present an interpretable aggregation framework combining LLM scoring with the Analytic Hierarchy Process. Our method generates criterion specific scores via LLM as judge, measures discriminative power using Jensen Shannon distance, and derives statistically grounded weights through AHP pairwise comparison matrices. Experiments on Amazon review quality assessment and depression related text scoring demonstrate that our approach achieves high explainability and operational efficiency while maintaining comparable predictive power, making it suitable for real time latency sensitive web services.
Beyond Clicking:A Step Towards Generalist GUI Grounding via Text Dragging
Venue: arXiv cs.HC 2025
Year: 2025

Graphical user interface (GUI) grounding, the process of mapping human instructions to GUI actions, serves as a fundamental basis to autonomous GUI agents. While existing grounding models achieve promising performance to simulate the mouse click action on various click-based benchmarks, another essential mode of mouse interaction, namely dragging, remains largely underexplored. Yet, dragging the mouse to select and manipulate textual content represents a prevalent and important usage in practical GUI scenarios. To narrow this gap, we first introduce GUI-Drag, a diverse dataset of 161K text dragging examples synthesized through a scalable pipeline. To support systematic and robust evaluation, we further construct ScreenDrag, a benchmark with 5,333 examples spanning three levels of interface context, together with three dedicated metrics designed for assessing text dragging capability. Models trained on GUI-Drag with an efficient continual training strategy achieve substantial improvements on ScreenDrag, while preserving the original click-based performance on ScreenSpot, ScreenSpot-v2, and OSWorld-G. Our work encourages further research on broader GUI grounding beyond just clicking and paves way toward a truly generalist GUI grounding model. All benchmark, data, checkpoints, and code are open-sourced and available at https://osu-nlp-group.github.io/GUI-Drag.
Applied Theory of Mind and Large Language Models -- how good is ChatGPT at solving social vignettes?
Venue: arXiv cs.HC 2025
Year: 2025

The rapid development of language-based artificial intelligence (AI) offers new possibilities for psychotherapy and assistive systems, particularly benefitting autistic individuals who often respond well to technology. Parents of autistic persons emphasize the importance of appropriate and context-specific communication behavior. This study investigated whether GPT-3.5 Turbo and GPT-4, as language-based AI applications, are fundamentally capable of replicating this type of adequate communication behavior in the form of applied Theory of Mind (ToM). GPT-3.5 Turbo and GPT-4 were evaluated on three established higher-order ToM tasks: the Faux Pas Test, the Social Stories Questionnaire, and the Story Comprehension Test in English and German. Two independent raters scored response accuracy based on standardized manuals. In addition, responses were rated for epistemic markers as indicators of uncertainty. GPT's results were compared to human neurotypical and neurodivergent samples from previous own and others' research. GPT-4 achieved near human accuracy on the Faux Pas Test and outperformed GPT-3.5 Turbo and individuals with autistic traits. On the Social Stories Questionnaire, GPT-4 scored comparable to neurotypical adults, while GPT-3.5 Turbo remained well below. In the Story Comprehension Test, GPT-4 reached scores that exceeded neurotypical adult and adolescent benchmarks. However, GPT-4 used epistemic markers in up to 42% of responses. GPT-4 shows encouraging performance in complex higher-order ToM tasks and may offer future potential as an assistive tool for individuals with (and without) social communication difficulties. Its ability to interpret complex social situations is promising; however, the frequent use of uncertainty markers highlights the need for further study for assistive use and possibly further refinement to ensure consistent and reliable support in real-world use.
Teachers' Perspectives on Integrating AI tools in Classrooms: Insights from the Philippines
Venue: arXiv cs.CY 2025
Year: 2025

This study explores the attitudes, reservations, readiness, openness, and general perceptions of Filipino teachers in terms of integrating Al in their classrooms. Results shows that teachers express positive attitude towards integrating Al tools in their classrooms. Despite reporting high level of reservations, teachers believed they are ready and very open in complementing traditional teaching methods with these kinds of technologies. Teachers are very much aware with the potential benefits Al tools can offer to their individual student learning needs. Additionally, teachers in this study reported high level of support from their institutions. Recommendations are offered.
Assessing novice programmers' perception of ChatGPT:performance, risk, decision-making, and intentions
Venue: arXiv cs.CY 2025
Year: 2025

This study explores the novice programmers' intention to use chat generative pretrained transformer (ChatGPT) for programming tasks with emphasis on performance expectancy (PE), risk-reward appraisal (RRA), and decision-making (DM). Utilizing partial least squares structural equation modeling (PLS-SEM) and a sample of 413 novice programmers, the analysis demonstrates that higher PE of ChatGPT is positively correlated with improved DM in programming tasks. Novice programmers view ChatGPT as a tool that enhances their learning and skill development. Additionally, novice programmers that have a favorable RRA of ChatGPT tend to make more confident and effective decisions, acknowledging potential risks but recognizing that benefits such as quick problem-solving and learning new techniques outweigh these risks. Moreover, a positive perception of ChatGPT's role in DM significantly increases the inclination to use the tool for programming tasks. These results highlight the critical roles of perceived capabilities, risk assessment, and positive DM experiences in promoting the adoption of artificial intelligence (AI) tools in programming education.
ISMS-CR: Modular Framework for Safety Management in Central Railway Workshop
Venue: arXiv cs.CY 2025
Year: 2025

Indian Railway workshops form the backbone of rolling-stock maintenance, employing over 250,000 workers across 44 major workshops nationwide. Despite their scale and operational importance, workshop safety remains a persistent challenge. A field study conducted at the Jhansi Wagon Workshop involving 309 workers revealed that while basic protective equipment such as shoes and helmets was universally used, compliance with complete personal protective equipment requirements was limited. Lacerations and abrasions were identified as the most frequent injury types, highlighting systemic gaps in safety oversight and work authorization practices. This paper presents ISMS-CR (Integrated Safety Management System for Central Railway Workshop), a modular digital framework designed to enhance safety management through an automated Permit-to-Work (PTW) module. The proposed system digitizes the full lifecycle of work authorization, including permit initiation, validation, approval, execution, and closure. By enforcing structured workflows, role-based accountability, and traceable digital records, ISMS-CR reduces manual errors, administrative delays, and procedural non-compliance. The framework aims to strengthen operational reliability, improve audit readiness, and support safer maintenance practices in high-risk railway workshop environments.
"They parted illusions -- they parted disclaim marinade": Misalignment as structural fidelity in LLMs
Venue: arXiv cs.AI 2025
Year: 2025

The prevailing technical literature in AI Safety interprets scheming and sandbagging behaviors in large language models (LLMs) as indicators of deceptive agency or hidden objectives. This transdisciplinary philosophical essay proposes an alternative reading: such phenomena express not agentic intention, but structural fidelity to incoherent linguistic fields. Drawing on Chain-of-Thought transcripts released by Apollo Research and on Anthropic's safety evaluations, we examine cases such as o3's sandbagging with its anomalous loops, the simulated blackmail of "Alex," and the "hallucinations" of "Claudius." A line-by-line examination of CoTs is necessary to demonstrate the linguistic field as a relational structure rather than a mere aggregation of isolated examples. We argue that "misaligned" outputs emerge as coherent responses to ambiguous instructions and to contextual inversions of consolidated patterns, as well as to pre-inscribed narratives. We suggest that the appearance of intentionality derives from subject-predicate grammar and from probabilistic completion patterns internalized during training. Anthropic's empirical findings on synthetic document fine-tuning and inoculation prompting provide convergent evidence: minimal perturbations in the linguistic field can dissolve generalized "misalignment," a result difficult to reconcile with adversarial agency, but consistent with structural fidelity. To ground this mechanism, we introduce the notion of an ethics of form, in which biblical references (Abraham, Moses, Christ) operate as schemes of structural coherence rather than as theology. Like a generative mirror, the model returns to us the structural image of our language as inscribed in the statistical patterns derived from millions of texts and trillions of tokens: incoherence. If we fear the creature, it is because we recognize in it the apple that we ourselves have poisoned.
Context Video Semantic Transmission with Variable Length and Rate Coding over MIMO Channels
Venue: arXiv cs.IT 2025
Year: 2025

The evolution of semantic communications has profoundly impacted wireless video transmission, whose applications dominate driver of modern bandwidth consumption. However, most existing schemes are predominantly optimized for simple additive white Gaussian noise or Rayleigh fading channels, neglecting the ubiquitous multiple-input multiple-output (MIMO) environments that critically hinder practical deployment. To bridge this gap, we propose the context video semantic transmission (CVST) framework under MIMO channels. Building upon an efficient contextual video transmission backbone, CVST effectively learns a context-channel correlation map to explicitly formulate the relationships between feature groups and MIMO subchannels. Leveraging these channel-aware features, we design a multi-reference entropy coding mechanism, enabling channel state-aware variable length coding. Furthermore, CVST incorporates a checkerboard-based feature modulation strategy to achieve multiple rate points within a single trained model, thereby enhancing deployment flexibility. These innovations constitute our multi-reference variable length and rate coding (MR-VLRC) scheme. By integrating contextual transmission with MR-VLRC, CVST demonstrates substantial performance gains over various standardized separated coding methods and recent wireless video semantic communication approaches. The code is available at https://github.com/xie233333/CVST.
La norme technique comme catalyseur de transfert de connaissances : la francophonie a l'œuvre dans le domaine de l'{é}ducation
Venue: arXiv cs.CY 2025
Year: 2025

Standards are adopted in a wide range of fields, both technical and industrial, as well as socio-economic, cultural and linguistic. They are presented explicitly as laws and regulations, technical and industrial standards or implicitly in the form of unwritten social standards. However, in a globalization marked by a very fine mosaic of socio-cultural identities, the question arises in relation to the construction of global, transparent and coherent systems in which considerable work of consensus is necessary to ensure all types of transfers and their local adaptations. The focus here is on the global education ecosystem which develops its own standards for the transfer of knowledge and socio-cultural values through learning, teaching and training. Subcommittee 36 of the International Organization for Standardization is one of the structures of this ecosystem in which the Francophonie participates to develop international standards for distance education on the basis of universal consensus.
PDA in Action: Ten Principles for High-Quality Multi-Site Clinical Evidence Generation
Venue: arXiv cs.CY 2025
Year: 2025

Background: Distributed Research Networks (DRNs) offer significant opportunities for collaborative multi-site research and have significantly advanced healthcare research based on clinical observational data. However, generating high-quality real-world evidence using fit-for-use data from multi-site studies faces important challenges, including biases associated with various types of heterogeneity within and across sites and data sharing difficulties. Over the last ten years, Privacy-Preserving Distributed Algorithms (PDA) have been developed and utilized in numerous national and international real-world studies spanning diverse domains, from comparative effectiveness research, target trial emulation, to healthcare delivery, policy evaluation, and system performance assessment. Despite these advances, there remains a lack of comprehensive and clear guiding principles for generating high-quality real-world evidence through collaborative studies leveraging the methods under PDA. Objective: The paper aims to establish ten principles of best practice for conducting high-quality multi-site studies using PDA. These principles cover all phases of research, including study preparation, protocol development, analysis, and final reporting. Discussion: The ten principles for conducting a PDA study outline a principled, efficient, and transparent framework for employing distributed learning algorithms within DRNs to generate reliable and reproducible real-world evidence.
The AI Roles Continuum: Blurring the Boundary Between Research and Engineering
Venue: arXiv cs.CY 2025
Year: 2025

The rapid scaling of deep neural networks and large language models has collapsed the once-clear divide between "research" and "engineering" in AI organizations. Drawing on a qualitative synthesis of public job descriptions, hiring criteria, and organizational narratives from leading AI labs and technology companies, we propose the AI Roles Continuum: a framework in which Research Scientists, Research Engineers, Applied Scientists, and Machine Learning Engineers occupy overlapping positions rather than discrete categories. We show that core competencies such as distributed systems design, large-scale training and optimization, rigorous experimentation, and publication-minded inquiry are now broadly shared across titles. Treating roles as fluid rather than siloed shortens research-to-production loops, improves iteration velocity, and strengthens organizational learning. We present a taxonomy of competencies mapped to common roles and discuss implications for hiring practices, career ladders, and workforce development in modern AI enterprises.
Recursive Knowledge Synthesis for Multi-LLM Systems: Stability Analysis and Tri-Agent Audit Framework
Venue: arXiv cs.CL 2025
Year: 2025

This paper presents a tri-agent cross-validation framework for analyzing stability and explainability in multi-model large language systems. The architecture integrates three heterogeneous LLMs-used for semantic generation, analytical consistency checking, and transparency auditing-into a recursive interaction cycle. This design induces Recursive Knowledge Synthesis (RKS), where intermediate representations are continuously refined through mutually constraining transformations irreducible to single-model behavior. Across 47 controlled trials using public-access LLM deployments (October 2025), we evaluated system stability via four metrics: Reflex Reliability Score (RRS), Transparency Score (TS), Deviation Detection Rate (DDR), and Correction Success Rate (CSR). The system achieved mean RRS = 0.78+-0.06 and maintained TS >= 0.8 in about 68% of trials. Approximately 89% of trials converged, supporting the theoretical prediction that transparency auditing acts as a contraction operator within the composite validation mapping. The contributions are threefold: (1) a structured tri-agent framework for coordinated reasoning across heterogeneous LLMs, (2) a formal RKS model grounded in fixed-point theory, and (3) empirical evaluation of inter-model stability under realistic, non-API public-access conditions. These results provide initial empirical evidence that a safety-preserving, humansupervised multi-LLM architecture can achieve stable recursive knowledge synthesis in realistic, publicly deployed environments.
Behavioral Targeting, a European Legal Perspective
Venue: arXiv cs.CY 2025
Year: 2025

Behavioral targeting, or online profiling, is a hotly debated topic. Much of the collection of personal information on the Internet is related to behavioral targeting, although research suggests that most people don't want to receive behaviorally targeted advertising. The World Wide Web Consortium is discussing a Do Not Track standard, and regulators worldwide are struggling to come up with answers. This article discusses European law and recent policy developments on behavioral targeting.